To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination predi...To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.展开更多
Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty,multi-frequency and nonlinearity for it is su...Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty,multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed.Therefore, this paper proposes a wind power prediction model combining multi-frequency combination and feature selection.Firstly, the variational mode decomposition(VMD) is used to decompose the wind power data, and the sub-components with different fluctuation characteristics are obtained and divided into high-, intermediate-, and low-frequency components according to their fluctuation characteristics. Then, a feature set including historical data of wind power and meteorological factors is established, which chooses the feature sets of each component by using the max-relevance and min-redundancy(m RMR) feature selection method based on mutual information selected from the above set. Each component and its corresponding feature set are used as an input set for prediction afterwards. Thereafter, the high-frequency input set is predicted using back propagation neural network(BPNN), and the intermediate-and low-frequency input sets are predicted using least squares support vector machine(LS-SVM). After obtaining the prediction results of each component, BPNN is used for integration to obtain the final predicted value of wind power, and the ramping rate is verified. Finally, through the comparison, it is found that the proposed model has higher prediction accuracy.展开更多
相对于系统级负荷,用户负荷具有基数小、波动性与随机性更强的特点,加大了用户负荷预测的难度。文章借助互信息与深度学习理论,提出了一种基于最大相关最小冗余(max-relevance and min-redundancy, mRMR)和长短期记忆网络(long-short te...相对于系统级负荷,用户负荷具有基数小、波动性与随机性更强的特点,加大了用户负荷预测的难度。文章借助互信息与深度学习理论,提出了一种基于最大相关最小冗余(max-relevance and min-redundancy, mRMR)和长短期记忆网络(long-short term memory networks, LSTM)的用户负荷短期预测模型。首先,采用mRMR算法对特征变量进行排序并选取合适的输入变量集合,mRMR既可以保证输入变量与目标值间互信息值最大,又使得变量间冗余性最小。接着,对选取的输入变量集合建立LSTM预测模型,LSTM能较好处理和预测延迟较长的时间序列,且不会存在梯度消失和梯度爆炸现象。最后,通过算例验证了所提算法的有效性。展开更多
文摘To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.
基金China National Tobacco Corporation Science and Technology Major Project(No:Zhong Yan Ban [2016] 259)National Key Research and Development Program of China(2016YFB0700504)~~
基金supported by the National Key Research and Development Program of China (No. 2016YFC0401409)the Research Fund of the State Key Laboratory of Eco-hydraulics in Northwest Arid Region,Xi’ an University of Technology (No. 2019KJCXTD-5)+1 种基金the Key Research and Development Plan of Shaanxi Province (No. 2018-ZDCXL-GY-10-04)the Natural Science Basic Research Program of Shaanxi (No. 2019JLZ-15)。
文摘Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty,multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed.Therefore, this paper proposes a wind power prediction model combining multi-frequency combination and feature selection.Firstly, the variational mode decomposition(VMD) is used to decompose the wind power data, and the sub-components with different fluctuation characteristics are obtained and divided into high-, intermediate-, and low-frequency components according to their fluctuation characteristics. Then, a feature set including historical data of wind power and meteorological factors is established, which chooses the feature sets of each component by using the max-relevance and min-redundancy(m RMR) feature selection method based on mutual information selected from the above set. Each component and its corresponding feature set are used as an input set for prediction afterwards. Thereafter, the high-frequency input set is predicted using back propagation neural network(BPNN), and the intermediate-and low-frequency input sets are predicted using least squares support vector machine(LS-SVM). After obtaining the prediction results of each component, BPNN is used for integration to obtain the final predicted value of wind power, and the ramping rate is verified. Finally, through the comparison, it is found that the proposed model has higher prediction accuracy.
文摘相对于系统级负荷,用户负荷具有基数小、波动性与随机性更强的特点,加大了用户负荷预测的难度。文章借助互信息与深度学习理论,提出了一种基于最大相关最小冗余(max-relevance and min-redundancy, mRMR)和长短期记忆网络(long-short term memory networks, LSTM)的用户负荷短期预测模型。首先,采用mRMR算法对特征变量进行排序并选取合适的输入变量集合,mRMR既可以保证输入变量与目标值间互信息值最大,又使得变量间冗余性最小。接着,对选取的输入变量集合建立LSTM预测模型,LSTM能较好处理和预测延迟较长的时间序列,且不会存在梯度消失和梯度爆炸现象。最后,通过算例验证了所提算法的有效性。
